Fiona Adamson

PEST Course: Brisbane

Description Where: EcoSciences Precinct, Dutton Park, Brisbane Who should attend: Both new and experienced modellers will benefit from the course, as well as anyone who would simply like to understand the theory and practice behind simulation-based data processing and environmental forecasting. Topic: “PEST” refers to a software package and to a suite of utility programs that supports it. […]

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Conceptual Model to Numerical Model

This tutorial explores the use of “conceptual points” as a precursor to model parameterisation. Expected hydraulic properties are provided at these conceptual points. Just as importantly, so-called “hyperparameters” are also ascribed to these points; these are used to characterise the nature of hydraulic property heterogeneity as it is likely to prevail in their vicinity. All

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Data Assimilation for a Simple Model

This tutorial explores construction of the interface between PEST/PEST++and a simple MODFLOW/MODPATH model, and how to then subject that model to history-matching and uncertainty analysis–including data space inversion (DSI).There is some overlap with a previous tutorial. However, there are also some important differences. Use of the PLPROC, TPL2PST and PESTPREP2 utilities are explained. The first

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Live session recordings

Week 1 NZ/AUS https://vimeo.com/866196071%20 This is the first recording from the NZ/AUS self-paced guided study course on Decision Support Groundwater Modeling with Python. Week 1 covered the Freyberg model, Bayes equation, and the PEST interface.Presenters: Jeremy White, Rui Hugman and Brioch Hemmings USA https://vimeo.com/866312617%20 This is the first recording from the USA self-paced guided study

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Webinar: Applied decision support groundwater modelling with python

GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support groundwater modelling, from introductions to concepts through to complete modelling workflows.  This webinar presented and discussed the design and content of these notebooks, including how to get started using them,

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Structural Overlay Parameters and PLPROC

Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted into a model. These features can be assigned to one or many model layers. Hydraulic properties can vary along and within them. If appropriate, the hydraulic properties associated with structural

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Worked Example: Structural Overlay Parameters in a Tunnel Model

Construction of a decision support groundwater model requires that parameters be adjustable, stochastic and representative of geological conditions. This can be difficult to achieve in complex hydrogeological environments where controls on hydraulic properties are both geological and structural in origin.This worked example report demonstrates use of the PLPROC parameter preprocessor in construction of a stochastic

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So we’ve ticked the uncertainty box. What happens next?

Date: 9th May 2023 (Recorded – see button below) John Doherty, Jeremy White and Catherine Moore presented issues that occupy the boundaries between uncertainty analysis and decision-making/policy-formulation. They discussed some of the problems that beset the making of decisions in an uncertain world. The talks are non-technical; the issues are important. They included: Uncertainties in

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Demystifying uncertainty and its policy repercussions

Date: 28th February 2023 Webinar recording This webinar had two presenters. The first is Chris Li (from CDM Smith). Chris delivered a talk entitled ‘The “Cinderella Syndrome” of groundwater modelling, and overcoming it through risk-orientated uncertainty analysis’ at the recent Australian Groundwater Conference. It was highly acclaimed. So we asked him to make his talk a little

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Data Space Inversion

This tutorial introduces data space inversion (DSI). DSI can be used to explore the uncertainties of predictions made by complex models with complicated hydraulic property fields. The model run burden is extremely low, and unrelated to the complexity of the complex model’s construction or parameterisation. There is considerable overlap between this tutorial and the “Four

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